Traditional saliency algorithms with a long history targeted at visual attention prediction, which refers to
the task of identifying the fixation points that human viewers would focus
on at first glance. The work of Itti et al. [1]}, which was inspired by the Koch and Ullman model [2]},
was one of the earliest computational models. Since then, many follow-up works [3]} have been proposed in this direction.
In recent decades, there is a new wave in saliency detection [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]} that concentrated on uniformly highlighting the most salient object regions in an image, starting with the works of [14]} and [15]}. The later methods, also named as salient object detection, are directly driven by
object-level tasks. In this study, we mainly overview the typical works of the first type of saliency models, since our method tries to predict human eye fixations over an image. We refer the reader to recent literatures ([16]} and [17]}) for more detailed overviews.
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